<?xml version="1.0" standalone="yes"?>
<Paper uid="H05-2015">
  <Title>THE MIT SPOKEN LECTURE PROCESSING PROJECT</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Over the past decade there has been increasing amounts of educational material being made available on-line. Projects such as MIT OpenCourse-Ware provide continuous worldwide access to educational materials to help satisfy our collective thirst for knowledge. While the majority of such material is currently text-based, we are beginning to see dramatic increases in the amount of audio and visual recordings of lecture material. Unlike text materials, untranscribed audio data can be tedious to browse, making it difficult to utilize the information fully without time-consuming data preparation. Moreover, unlike some other forms of spoken communication such as telephone conversations or television and radio broadcasts, lecture processing has until recently received little attention or benefit from the development of human language technology. The single biggest effort, to date, is on-going work in Japan using the Corpus of Spontaneous Japanese [1,3,4].</Paragraph>
    <Paragraph position="1"> Lectures are particularly challenging for automatic speech recognizers because the vocabulary used within a lecture can be very technical and specialized, yet the speaking style can be very spontaneous. As a result, even if parallel text materials are available in the form of textbooks or related papers, there are significant linguistic differences between written and oral communication styles. Thus, it is a challenge to predict how a written passage might be spoken, and vice versa.</Paragraph>
    <Paragraph position="2"> By helping to focus a research spotlight on spoken lecture material, we hope to begin to overcome these and many other fundamental issues.</Paragraph>
    <Paragraph position="3"> While audio-visual lecture processing will perhaps be ultimately most useful, we have initially focused our attention on the problem of spoken lecture processing. Within this realm there are many challenging research issues pertaining to the development of effective automatic transcription, indexing, and summarization. For this project, our goals have been to a) help create a corpus of spoken lecture material for the research community, b) analyze this corpus to better understand the linguistic characteristics of spoken lectures, c) perform speech recognition and information retrieval experiments on these data to benchmark performance on these data, d) develop a prototype spoken lecture processing server that will allow educators to automatically annotate their recorded lecture data, and e) develop prototype software that will allow students to browse the resulting annotated lectures.</Paragraph>
  </Section>
class="xml-element"></Paper>